[HTML][HTML] Consensus clustering and functional interpretation of gene-expression data
Microarray analysis using clustering algorithms can suffer from lack of inter-method
consistency in assigning related gene-expression profiles to clusters. Obtaining a
consensus set of clusters from a number of clustering methods should improve confidence in
gene-expression analysis. Here we introduce consensus clustering, which provides such an
advantage. When coupled with a statistically based gene functional analysis, our method
allowed the identification of novel genes regulated by NFκB and the unfolded protein …
consistency in assigning related gene-expression profiles to clusters. Obtaining a
consensus set of clusters from a number of clustering methods should improve confidence in
gene-expression analysis. Here we introduce consensus clustering, which provides such an
advantage. When coupled with a statistically based gene functional analysis, our method
allowed the identification of novel genes regulated by NFκB and the unfolded protein …
Abstract
Microarray analysis using clustering algorithms can suffer from lack of inter-method consistency in assigning related gene-expression profiles to clusters. Obtaining a consensus set of clusters from a number of clustering methods should improve confidence in gene-expression analysis. Here we introduce consensus clustering, which provides such an advantage. When coupled with a statistically based gene functional analysis, our method allowed the identification of novel genes regulated by NFκB and the unfolded protein response in certain B-cell lymphomas.
Springer